[2511.13111] NuBench: An Open Benchmark for Deep Learning-Based Event Reconstruction in Neutrino Telescopes

High Energy Physics - Experiment arXiv:2511.13111 (hep-ex) [Submitted on 17 Nov 2025 (v1), last revised 26 Feb 2026 (this version, v3)] Title:NuBench: An Open Benchmark for Deep Learning-Based Event Reconstruction in Neutrino Telescopes Authors:Rasmus F. Orsoe, Stephan Meighen-Berger, Jeffrey Lazar, Jorge Prado, Ivan Mozun-Mateo, Aske Rosted, Philip Weigel, Arturo Llorente Anaya View a PDF of the paper titled NuBench: An Open Benchmark for Deep Learning-Based Event Reconstruction in Neutrino Telescopes, by Rasmus F. Orsoe and 7 other authors View PDF Abstract:Neutrino telescopes are large-scale detectors designed to observe Cherenkov radiation produced from neutrino interactions in water or ice. They exist to identify extraterrestrial neutrino sources and to probe fundamental questions pertaining to the elusive neutrino itself. A central challenge common across neutrino telescopes is to solve a series of inverse problems known as event reconstruction, which seeks to resolve properties of the incident neutrino, based on the detected Cherenkov light. In recent times, significant efforts have been made in adapting advances from deep learning research to event reconstruction, as such techniques provide several benefits over traditional methods. While a large degree of similarity in reconstruction needs and low-level data exists, cross-experimental collaboration has been hindered by a lack of diverse open-source datasets for comparing methods. We present NuBench, an open benchm...